Abstract: Understanding users' gait preferences of a lower-body exoskeleton requires
optimizing over the high-dimensional gait parameter space. However, existing
preference-based learning methods have only explored low-dimensional domains
due to computational limitations. To learn user preferences in high dimensions,
this work presents LineCoSpar, a human-in-the-loop preference-based framework
that enables optimization over many parameters by iteratively exploring
one-dimensional subspaces. Additionally, this work identifies gait attributes
that characterize broader preferences across users. In simulations and human
trials, we empirically verify that LineCoSpar is a sample-efficient approach
for high-dimensional preference optimization. Our analysis of the experimental
data reveals a correspondence between human preferences and objective measures
of dynamic stability, while also highlighting inconsistencies in the utility
functions underlying different users' gait preferences. This has implications
for exoskeleton gait synthesis, an active field with applications to clinical
use and patient rehabilitation.